OpenRaiser/Intern-Atlas huggingface dataset:
Latent Networks Fusion based Model for Event Recommendation in Offline Ephemeral Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

With the growing amount of mobile social media, offline ephemeral social networks (OffESNs) are receiving more and more attentions. Offline ephemeral social networks (OffESNs) are the networks created ad-hoc at a specific location for a specific purpose and lasting for short period of time, relying on mobile social media such as Radio Frequency Identification (RFID) and Bluetooth devices. The primary purpose of people in the OffESNs is to acquire and share information via attending prescheduled events. Event Recommendation over this kind of networks can facilitate attendees on selecting the prescheduled events and organizers on making resource planning. However, because of lack of users preference and rating information, as well as explicit social relations, both rating based traditional recommendation methods and social-trust based recommendation methods can no longer work well to recommend events in the OffESNs. To address the challenges such as how to derive users latent preferences and social relations and how to fuse the latent information in a unified model, we first construct two heterogeneous interaction social networks, an event participation network and a physical proximity network. Then, we use them to derive users latent preferences and latent networks on social relations, including like-minded peers, co-attendees and friends. Finally, we propose an LNF (Latent Networks Fusion) model under a pairwise factor graph to infer event attendance probabilities for recommendation. Experiments on an RFID-based real conference dataset have demonstrated the effectiveness of the proposed model compared with typical solutions.
Multimodal Approach for Video Surveillance Indexing and Retrieval
Venue: arXiv cs.MM 2013
Year: 2013

In this paper, we present an overview of a multimodal system to indexing and searching video sequence by the content that has been developed within the REGIMVid project. A large part of our system has been developed as part of TRECVideo evaluation. The MAVSIR platform provides High-level feature extraction from audio-visual content and concept/event-based video retrieval. We illustrate the architecture of the system as well as provide an overview of the descriptors supported to date. Then we demonstrate the usefulness of the toolbox in the context of feature extraction, concepts/events learning and retrieval in large collections of video surveillance dataset. The results are encouraging as we are able to get good results on several event categories, while for all events we have gained valuable insights and experience.
Spatial-Aware Dictionary Learning for Hyperspectral Image Classification
Venue: arXiv cs.CV 2013
Year: 2013

This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of a spectral sample, with the goal of hyperspectral image classification. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model each pixel with a linear combination of a few dictionary elements learned from the data. Since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of each contextual group. The sparse coefficients are then used for classification using a linear SVM. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral-resolution samples.
The Networks Are Out There: Building Cultural and Economic Resilience Through Informal Communities of Practice
Venue: arXiv cs.SI 2013
Year: 2013

This paper explores the possibilities offered by informal communities of practice to operate as "prototyping spaces" for innovation in the context of developing economies. It begins by looking at the concept of "economic complexity" and how it is useful in both guiding the priorities and evaluating the challenges developing economies face when attempting to drive growth and build measures of resilience, and raises the question of how these economies can both introduce higher complexity activities while compensating for latecomer disadvantages versus more complex economies. It then examines in detail the case of the Twin Eagles Group, a Peruvian video game development community in the 1990s, and how they reverse engineered technologies and global practices to pursue their creative objectives. Based on this case, it concludes by laying out some of the methodological challenges associated to researching this sort of community because of the multi-sited nature of their activities.
Dynamics of Human Social Networks: People, Time, Relationships, and Places
Venue: arXiv cs.SI 2013
Year: 2013

The availability of advanced social interaction sensing technologies provides fine grained data for social network analysis. Although traditional methods of gathering social network data may be subject to human ability to recall social details, people's rating of their closeness to persons in their network is important. This study assesses the relationship amidst closeness ratings, sensed interactions and shared places of recreation. The study found that people tend to give high closeness ratings to people with whom they spend more time. Shared places are correlated to social closeness ratings but not to length of interactions. The results of this study highlight the importance of sensed interactions and closeness ratings.
Performance and Optimization Abstractions for Large Scale Heterogeneous Systems in the Cactus/Chemora Framework
Venue: arXiv cs.DC 2013
Year: 2013

We describe a set of lower-level abstractions to improve performance on modern large scale heterogeneous systems. These provide portable access to system- and hardware-dependent features, automatically apply dynamic optimizations at run time, and target stencil-based codes used in finite differencing, finite volume, or block-structured adaptive mesh refinement codes. These abstractions include a novel data structure to manage refinement information for block-structured adaptive mesh refinement, an iterator mechanism to efficiently traverse multi-dimensional arrays in stencil-based codes, and a portable API and implementation for explicit SIMD vectorization. These abstractions can either be employed manually, or be targeted by automated code generation, or be used via support libraries by compilers during code generation. The implementations described below are available in the Cactus framework, and are used e.g. in the Einstein Toolkit for relativistic astrophysics simulations.
The Performance of Paxos and Fast Paxos
Venue: arXiv cs.DC 2013
Year: 2013

Paxos and Fast Paxos are optimal consensus algorithms that are simple and elegant, while suitable for efficient implementation. In this paper, we compare the performance of both algorithms in failure-free and failure-prone runs using Treplica, a general replication toolkit that implements these algorithms in a modular and efficient manner. We have found that Paxos outperforms Fast Paxos for small number of replicas and that collisions are not the cause of this performance difference.
ManyClaw: Slicing and dicing Riemann solvers for next generation highly parallel architectures
Venue: arXiv cs.CE 2013
Year: 2013

Next generation computer architectures will include order of magnitude more intra-node parallelism; however, many application programmers have a difficult time keeping their codes current with the state-of-the-art machines. In this context, we analyze Hyperbolic PDE solvers, which are used in the solution of many important applications in science and engineering. We present ManyClaw, a project intended to explore the exploitation of intra-node parallelism in hyperbolic PDE solvers via the Clawpack software package for solving hyperbolic PDEs. Our goal is to separate the low level parallelism and the physical equations thus providing users the capability to leverage intra-node parallelism without explicitly writing code to take advantage of newer architectures.
ForestClaw: Hybrid forest-of-octrees AMR for hyperbolic conservation laws
Venue: arXiv cs.MS 2013
Year: 2013

We present a new hybrid paradigm for parallel adaptive mesh refinement (AMR) that combines the scalability and lightweight architecture of tree-based AMR with the computational efficiency of patch-based solvers for hyperbolic conservation laws. The key idea is to interpret each leaf of the AMR hierarchy as one uniform compute patch in $\sR^d$ with $m^d$ degrees of freedom, where $m$ is customarily between 8 and 32. Thus, computation on each patch can be optimized for speed, while we inherit the flexibility of adaptive meshes. In our work we choose to integrate with the p4est AMR library since it allows us to compose the mesh from multiple mapped octrees and enables the cubed sphere and other nontrivial multiblock geometries. We describe aspects of the parallel implementation and close with scalings for both MPI-only and OpenMP/MPI hybrid runs, where the largest MPI run executes on 16,384 CPU cores.
A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
Venue: arXiv cs.NE 2013
Year: 2013

Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods.
ALOHA Random Access that Operates as a Rateless Code
Venue: arXiv cs.IT 2013
Year: 2013

Various applications of wireless Machine-to-Machine (M2M) communications have rekindled the research interest in random access protocols, suitable to support a large number of connected devices. Slotted ALOHA and its derivatives represent a simple solution for distributed random access in wireless networks. Recently, a framed version of slotted ALOHA gained renewed interest due to the incorporation of successive interference cancellation (SIC) in the scheme, which resulted in substantially higher throughputs. Based on similar principles and inspired by the rateless coding paradigm, a frameless approach for distributed random access in slotted ALOHA framework is described in this paper. The proposed approach shares an operational analogy with rateless coding, expressed both through the user access strategy and the adaptive length of the contention period, with the objective to end the contention when the instantaneous throughput is maximized. The paper presents the related analysis, providing heuristic criteria for terminating the contention period and showing that very high throughputs can be achieved, even for a low number for contending users. The demonstrated results potentially have more direct practical implications compared to the approaches for coded random access that lead to high throughputs only asymptotically.
Logical analysis of natural language semantics to solve the problem of computer understanding
Venue: arXiv cs.CL 2013
Year: 2013

An object--oriented approach to create a natural language understanding system is considered. The understanding program is a formal system built on the base of predicative calculus. Horn's clauses are used as well--formed formulas. An inference is based on the principle of resolution. Sentences of natural language are represented in the view of typical predicate set. These predicates describe physical objects and processes, abstract objects, categories and semantic relations between objects. Predicates for concrete assertions are saved in a database. To describe the semantics of classes for physical objects, abstract concepts and processes, a knowledge base is applied. The proposed representation of natural language sentences is a semantic net. Nodes of such net are typical predicates. This approach is perspective as, firstly, such typification of nodes facilitates essentially forming of processing algorithms and object descriptions, secondly, the effectiveness of algorithms is increased (particularly for the great number of nodes), thirdly, to describe the semantics of words, encyclopedic knowledge is used, and this permits essentially to extend the class of solved problems.
The Wisdom of Citing Scientists
Venue: arXiv cs.DL 2013
Year: 2013

This Brief Communication discusses the benefits of citation analysis in research evaluation based on Galton's "Wisdom of Crowds" (1907). Citations are based on the assessment of many which is why they can be ascribed a certain amount of accuracy. However, we show that citations are incomplete assessments and that one cannot assume that a high number of citations correlate with a high level of usefulness. Only when one knows that a rarely cited paper has been widely read is it possible to say (strictly speaking) that it was obviously of little use for further research. Using a comparison with 'like' data, we try to determine that cited reference analysis allows a more meaningful analysis of bibliometric data than times-cited analysis.
Pattern Language for Good Old Future From Japanese Culture
Venue: arXiv cs.CY 2013
Year: 2013

Having developed greatly over millennium under its culture, the ancient buildings and old town atmospheres maintain a quality of comfort. However, people only appreciate the "good old" quality and do not think further about the rational reasons why they feel comfort in it. This keeps them from creating their own things and models with good old quality, relying on the imported western thinking and methods as a result of modernization. Since people are now struggling under the imbalanced and complex society, we believe that support is needed for generating things and frameworks with good old quality in modern time situations and scenes.
The Course Design To Develop Meta-Cognitive Skills for Collaborative Learning Through Tool-Assisted Discourse Analysis
Venue: arXiv cs.CY 2013
Year: 2013

This paper presents the course design titled "Learning Management" of which the goal is to "learn collaborative learning" for a first-year undergraduate student. The objective of the class design is to help transform the student's belief of learning from a passive, individual model to an active, collaborative model which is supported by the concept of "Knowledge Building" or "Constructive Interaction". We conducted an empirical study where the students analyzed their own discourse with KBDeX which is the software they used to assist their analysis in the experimental group whereas the students in the control group reflected their project activities in their own way. We examined the transformation of their beliefs through the qualitative analysis of their reports after the course. The results showed that the design led to the transforming of their learning beliefs from "just experiences of the participation of the collaborative learning" to "active contribution for collaborative knowledge creation". The course succeeded in changing the students' preferences about collaborative learning from negative to positive as well.
The Deterministic Capacity of Relay Networks with Relay Private Messages
Venue: arXiv cs.IT 2013
Year: 2013

We study the capacity region of a deterministic 4-node network, where 3 nodes can only communicate via the fourth one. However, the fourth node is not merely a relay since it can exchange private messages with all other nodes. This situation resembles the case where a base station relays messages between users and delivers messages between the backbone system and the users. We assume an asymmetric scenario where the channel between any two nodes is not reciprocal. First, an upper bound on the capacity region is obtained based on the notion of single sided genie. Subsequently, we construct an achievable scheme that achieves this upper bound using a superposition of broadcasting node 4 messages and an achievable "detour" scheme for a reduced 3-user relay network.
OFF-Set: One-pass Factorization of Feature Sets for Online Recommendation in Persistent Cold Start Settings
Venue: arXiv cs.LG 2013
Year: 2013

One of the most challenging recommendation tasks is recommending to a new, previously unseen user. This is known as the 'user cold start' problem. Assuming certain features or attributes of users are known, one approach for handling new users is to initially model them based on their features. Motivated by an ad targeting application, this paper describes an extreme online recommendation setting where the cold start problem is perpetual. Every user is encountered by the system just once, receives a recommendation, and either consumes or ignores it, registering a binary reward. We introduce One-pass Factorization of Feature Sets, OFF-Set, a novel recommendation algorithm based on Latent Factor analysis, which models users by mapping their features to a latent space. Furthermore, OFF-Set is able to model non-linear interactions between pairs of features. OFF-Set is designed for purely online recommendation, performing lightweight updates of its model per each recommendation-reward observation. We evaluate OFF-Set against several state of the art baselines, and demonstrate its superiority on real ad-targeting data.
A Survey of Current Trends in Distributed, Grid and Cloud Computing
Venue: arXiv cs.DC 2013
Year: 2013

Through the 1990s to 2012 the internet changed the world of computing drastically. It started its journey with parallel computing after it advanced to distributed computing and further to grid computing. And in present scenario it creates a new world which is pronounced as a Cloud Computing [1]. These all three terms have different meanings. Cloud computing is based on backward computing schemes like cluster computing, distributed computing, grid computing and utility computing. The basic concept of cloud computing is virtualization. It provides virtual hardware and software resources to various requesting programs. This paper gives a detailed description about cluster computing, grid computing and cloud computing and gives an insight of some implementations of the same. We try to list the inspirations for the advent of all these technologies. We also account for some present scenario faults of grid computing and also discuss new cloud computing projects which are being managed by the Government of India for learning. The paper also reviews the existing work and covers (analytically), to some extent, some innovative ideas that can be implemented.
An Indoor Fingerprinting Localization Approach for ZigBee Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

Location tracking systems are increasingly becoming the focus of research in the field of Wireless Sensor Network (WSN). Received Signal Strength (RSS)-based localization systems are at the forefront of tracking research applications. Radio location fingerprinting is one of the most promising indoor positioning approaches due to its powerful in terms of accuracy and cost. However, fingerprinting systems require the collection of a large number of reference points in the tracking area to achieve reasonable localization accuracy. In this paper, we propose a fingerprinting localization approach based on a RSS technique. The proposed system does not require gathering a large number of reference points and offers good localization accuracy indoors. The implemented approach is based on dividing the tracking area into subareas and assigning a unique feature to each subarea through ranging the RSS values from different reference points. In order to test the proposed system's efficiency, a number of real experiments have been conducted using Jennic sensor nodes.
Semantic Computing of Moods Based on Tags in Social Media of Music
Venue: arXiv cs.MM 2013
Year: 2013

Social tags inherent in online music services such as Last.fm provide a rich source of information on musical moods. The abundance of social tags makes this data highly beneficial for developing techniques to manage and retrieve mood information, and enables study of the relationships between music content and mood representations with data substantially larger than that available for conventional emotion research. However, no systematic assessment has been done on the accuracy of social tags and derived semantic models at capturing mood information in music. We propose a novel technique called Affective Circumplex Transformation (ACT) for representing the moods of music tracks in an interpretable and robust fashion based on semantic computing of social tags and research in emotion modeling. We validate the technique by predicting listener ratings of moods in music tracks, and compare the results to prediction with the Vector Space Model (VSM), Singular Value Decomposition (SVD), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). The results show that ACT consistently outperforms the baseline techniques, and its performance is robust against a low number of track-level mood tags. The results give validity and analytical insights for harnessing millions of music tracks and associated mood data available through social tags in application development.
Tree dynamics for peer-to-peer streaming
Venue: arXiv cs.DS 2013
Year: 2013

This paper presents an asynchronous distributed algorithm to manage multiple trees for peer-to-peer streaming in a flow level model. It is assumed that videos are cut into substreams, with or without source coding, to be distributed to all nodes. The algorithm guarantees that each node receives sufficiently many substreams within delay logarithmic in the number of peers. The algorithm works by constantly updating the topology so that each substream is distributed through trees to as many nodes as possible without interference. Competition among trees for limited upload capacity is managed so that both coverage and balance are achieved. The algorithm is robust in that it efficiently eliminates cycles and maintains tree structures in a distributed way. The algorithm favors nodes with higher degree, so it not only works for live streaming and video on demand, but also in the case a few nodes with large degree act as servers and other nodes act as clients. A proof of convergence of the algorithm is given assuming instantaneous update of depth information, and for the case of a single tree it is shown that the convergence time is stochastically tightly bounded by a small constant times the log of the number of nodes. These theoretical results are complemented by simulations showing that the algorithm works well even when most assumptions for the theoretical tractability do not hold.